The thesis addresses time-domain inverse electromagneticscattering for determining unknown characteristics of an objectfrom observations of the scattered .eld. Applications includenon-destructive characterization of media and optimization ofmaterial properties, for example the design of radar absorbingmaterials.A nother interesting application is the parameteroptimization of subcell models to avoid detailed modeling ofcomplex geometries.

The inverse problem is formulated as an optimal controlproblem where the cost function to be minimized is thedi.erence between the estimated and observed .elds, and thecontrol parameters are the unknown object characteristics. Theproblem is solved in a deterministic gradient-basedoptimization algorithm using a parallel 2D FDTD scheme for thedirect problem.This approach is computationally intensive sincethe direct problem needs to be solved in every optimizationiteration in order to compute an estimated .eld.H ighlyaccurate analytical gradients are computed from the adjointformulation.In addition to giving better accuracy than .nitedi.erences, the analytical gradients also have the advantage ofonly requiring one direct and one adjoint problem to be solvedregardless of the number of parameters.

When absorbing boundary conditions are used to truncate thecomputational domain, the equations are non-reversible and theentire time-history of the direct solution needs to be storedfor the gradient computation.Ho wever, using an additionaldirect simulation and a restart procedure it is possible tokeep the storage at an acceptable level.

The inverse method has been successfully applied to a widerange of industrial problems within the European project,IMPACT (Inverse Methods for Wave Propagation Applications inTime-Domain).T he results presented here includecharacterization of layered dispersive media, determination ofparameters in subcell models for thin sheets and narrow slotsand optimization problems where the observed .eld is given bydesign objectives.

A voting system is defined as a procedure through which political power is distributed among candidates - from the ballot box to the parliament. This essay specifically seeks to contrast the Single Transferable Vote system with two other voting algorithms (Modified Sainte-Laguë and First-Past-The-Post), by constructing Java implementations of the algorithms and running example data through them. Thus, the suitability of a possible real-life implementation of the Single Transferable Vote method in a Swedish parliament context is evaluated. Furthermore, an alternative version of the original STV method which has been modified to fit these conditions is suggested. The effects of such an implementation on election outcomes are not entirely conclusive, and the conclusion is that more research is needed before a definite evaluation can be made.

259. Aberer, André

et al.

Stamatakis, Alexis

Ronquist, Fredrik

Swedish Museum of Natural History, Department of Bioinformatics and Genetics.

The success of the P2P idea has created a huge diversity of approaches, among which overlay networks, for example, Gnutella, Kazaa, Chord, Pastry, Tapestry, P-Grid, or DKS, have received specific attention from both developers and researchers. A wide variety of algorithms, data structures, and architectures have been proposed. The terminologies and abstractions used, however, have become quite inconsistent since the P2P paradigm has attracted people from many different communities, e.g., networking, databases, distributed systems, graph theory, complexity theory, biology, etc. In this paper we propose a reference model for overlay networks which is capable of modeling different approaches in this domain in a generic manner. It is intended to allow researchers and users to assess the properties of concrete systems, to establish a common vocabulary for scientific discussion, to facilitate the qualitative comparison of the systems, and to serve as the basis for defining a standardized API to make overlay networks interoperable.

This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims.

This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.

Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

Context: Maritime Surveillance (MS) has received increased attention from a civilian perspective in recent years. Anomaly detection (AD) is one of the many techniques available for improving the safety and security in the MS domain. Maritime authorities utilize various confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new sources of data for MS. These newly identified data sources, which provide publicly accessible data, are the open data sources. Taking advantage of the open data sources in addition to the traditional sources of data in the AD process will increase the accuracy of the MS systems. Objectives: The goal is to investigate the potential open data as a complementary resource for AD in the MS domain. To achieve this goal, the first step is to identify the applicable open data sources for AD. Then, a framework for AD based on the integration of open and closed data sources is proposed. Finally, according to the proposed framework, an AD system with the ability of using open data sources is developed and the accuracy of the system and the validity of its results are evaluated. Methods: In order to measure the system accuracy, an experiment is performed by means of a two stage random sampling on the vessel traffic data and the number of true/false positive and negative alarms in the system is verified. To evaluate the validity of the system results, the system is used for a period of time by the subject matter experts from the Swedish Coastguard. The experts check the detected anomalies against the available data at the Coastguard in order to obtain the number of true and false alarms. Results: The experimental outcomes indicate that the accuracy of the system is 99%. In addition, the Coastguard validation results show that among the evaluated anomalies, 64.47% are true alarms, 26.32% are false and 9.21% belong to the vessels that remain unchecked due to the lack of corresponding data in the Coastguard data sources. Conclusions: This thesis concludes that using open data as a complementary resource for detecting anomalous behavior in the MS domain is not only feasible but also will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.

Recent work has shown that convolutional networks can successfully handle time series as input in various different problems. This thesis embraces this observation and introduces a new method combining machine learning techniques in order to create profitable trading strategies. The method addresses a binary classification problem: given a specific time, access to prices before this moment and an exit policy, the goal is to forecast the next price movement. The classification method is based on convolutional networks combining two major improvements: a special form of bagging and a weight propagation, to enhance the accuracy and reduce the overall variance of the model. The rolling learning and the convolutional layers are able to exploit the time dependency to strongly improve the trading strategy. The presented architecture is able to surpass the expert traders.

Context. System Development Methodologies (SDM’s) have been an area of intensive research in the field of software engineering. Different software organisations adopt different development methodologies and use different development practices. The frequency of usage of development practices and acceptance factors for adoption of development methodology are crucial for software organisations. The factors of acceptance and development practices differ across geographical locations. Many challenges have been presented in the literature with respect to the mismatch of the development practices across organisations while collaborating across organisations in distributed development. There is no considerable amount of research done in context of differences across development practices and acceptance factors for adoption of a particular development methodology. Objectives. The primary objectives of the research are to find out a) differences in (i) practice usage (ii) acceptance factors such as organisational, social and cultural b) explore the reasons for the differences and also investigate consequences of such differences while collaborating, across organisations located in India and Sweden. Methods. A literature review was conducted by searching in scientific databases for identifying common agile and plan-driven development practices and acceptance theories for development methodologies. Survey was conducted across organisations located in India and Sweden to find out the usage frequency of development practices and acceptance factors. Ten interviews were conducted to investigate, reasons for differences and consequences of differences from the software practitioners from organisations located in India and Sweden. Literature evidences were used to support the results collected from interviews. Results. From the survey, organisations in India have adopted a higher frequency of plan driven practices when compared to Sweden and agile practices were adopted at higher frequency in Sweden when compared to India. The number of organisations adopting "pure agile" methodologies have been significantly higher in Sweden. There was significant differences were found across the acceptance factors such as cultural, organisational, image and career factors between India and Sweden. The factors such as cultural, social, human, business and organisational factors are responsible for such differences across development practices and acceptance factors. Challenges related to communication, coordination and control were found due to the differences, while collaborating between Indian and Sweden sites. Conclusions. The study signifies the importance of identifying the frequency of development practices and also the acceptance factors responsible for adoption of development methodologies in the software organisations. The mismatch between these practices will led to various challenges. The study draws insights into various non-technical factors such as cultural, human, organisational, business and social while collaborating between organisations. Variations across these factors will lead to many coordination, communication and control issues. Keywords: Development Practices, Agile Development, Plan Driven Development, Acceptance Factors, Global Software Development.

How do Swedish tweens (10–14 years old) understand and experience the writing of their online identities? How are such intertwined identity markers as gender and age expressed and negotiated? To find some answers to these questions, participants in this study were asked to write a story about the use of online web communities on pre-prepared paper roundels with buzzwords in the margins to inspire them. Content analysis of these texts using the constant comparative method showed that the main factors determining how online communities are understood and used are the cultural age and gender of the user. Both girls and boys chat online, but girls more often create blogs while boys more often play games. Gender was increasingly emphasised with age; but whereas boys aged 14 described themselves as sexually active and even users of pornography, girls of the same age described themselves as shocked and repelled by pornography and fearful of sexual threats. In this investigation an intersectionalist frame of reference is used to elucidate the intertwined power differentials and identity markers of the users' peer group situation.

Byzantine Fault Tolerant protocols are complicated and hard to implement.Today’s software industry is reluctant to adopt these protocols because of thehigh overhead of message exchange in the agreement phase and the high resourceconsumption necessary to tolerate faults (as 3 f + 1 replicas are required totolerate f faults). Moreover, total ordering of messages is needed by mostclassical protocols to provide strong consistency in both agreement and executionphases. Research has improved throughput of the execution phase by introducingconcurrency using modern multicore infrastructures in recent years. However,improvements to the agreement phase remains an open area.

Byzantine Fault Tolerant systems use State Machine Replication to tolerate awide range of faults. The approach uses leader based consensus algorithms for thedeterministic execution of service on all replicas to make sure all correct replicasreach same state. For this purpose, several algorithms have been proposed toprovide total ordering of messages through an elected leader. Usually, a singleleader is considered to be a bottleneck as it cannot provide the desired throughputfor real-time software services. In order to achieve a higher throughput there is aneed for a solution which can execute multiple consensus rounds concurrently.

We present a solution that enables multiple consensus rounds in parallel bychoosing multiple leaders. By enabling concurrent consensus, our approach canexecute several requests in parallel. In our approach we incorporate applicationspecific knowledge to split the total order of events into multiple partial orderswhich are causally consistent in order to ensure safety. Furthermore, a dependencycheck is required for every client request before it is assigned to a particular leaderfor agreement. This methodology relies on optimistic prediction of dependenciesto provide higher throughput. We also propose a solution to correct the course ofexecution without rollbacking if dependencies were wrongly predicted.

Our evaluation shows that in normal cases this approach can achieve upto 100% higher throughput than conventional approaches for large numbers ofclients. We also show that this approach has the potential to perform better incomplex scenarios

This Master’s thesis is aimed at improving the management of artifacts in the context of a joint-project between Jönköping University with the SEMCO project and industrial partner, a company involved in developing software for safety components. Both have a slightly distinct interest but this project can serve both parties.

Nowadays feature modelling is efficient way for domain analysis. The purpose of this master thesis is to analysis existing four popular feature diagrams, to find out commonalities between each of them and conclude results to give suggestions of how to use existing notation systems efficiently and according to situations.

The developed software based on knowledge established from research analysis. Two notation systems which are suggested in research part of the thesis report are implemented in the developed software “NotationManager”. The development procedures are also described and developer choices are mentioned along with the comparisons according to the situations

Scope of the research part as well as development is discussed. Future work for developed solution is also suggested.

We prove the first non-trivial performance ratios strictly above 0.5 for weighted versions of the oblivious matching problem. Even for the unweighted version, since Aronson, Dyer, Frieze, and Suen first proved a non-trivial ratio above 0.5 in the mid-1990s, during the next twenty years several attempts have been made to improve this ratio, until Chan, Chen, Wu and Zhao successfully achieved a significant ratio of 0.523 very recently (SODA 2014). To the best of our knowledge, our work is the first in the literature that considers the node-weighted and edge-weighted versions of the problem in arbitrary graphs (as opposed to bipartite graphs). (1) For arbitrary node weights, we prove that a weighted version of the Ranking algorithm has ratio strictly above 0.5. We have discovered a new structural property of the ranking algorithm: if a node has two unmatched neighbors at the end of algorithm, then it will still be matched even when its rank is demoted to the bottom. This property allows us to form LP constraints for both the node-weighted and the unweighted oblivious matching problems. As a result, we prove that the ratio for the node-weighted case is at least 0.501512. Interestingly via the structural property, we can also improve slightly the ratio for the unweighted case to 0.526823 (from the previous best 0.523166 in SODA 2014). (2) For a bounded number of distinct edge weights, we show that ratio strictly above 0.5 can be achieved by partitioning edges carefully according to the weights, and running the (unweighted) Ranking algorithm on each part. Our analysis is based on a new primal-dual framework known as matching coverage, in which dual feasibility is bypassed. Instead, only dual constraints corresponding to edges in an optimal matching are satisfied. Using this framework we also design and analyze an algorithm for the edge-weighted online bipartite matching problem with free disposal. We prove that for the case of bounded online degrees, the ratio is strictly above 0.5.

In today's world the amount of collected data increases every day, this is a trend which is likely to continue. At the same time the potential value of the data does also increase due to the constant development and improvement of hardware and software. However, in order to gain insights, make decisions or train accurate machine learning models we want to ensure that the data we collect is of good quality. There are many definitions of data quality, in this thesis we focus on the accuracy aspect.

One method which can be used to ensure accurate data is to monitor for and alert on anomalies. In this thesis we therefore suggest a method which, based on historic values, is able to detect anomalies in time series as new values arrive. The method consists of two parts, forecasting the next value in the time series using Holt-Winters method and comparing the residual to an estimated Gaussian distribution.

The suggested method is evaluated in two steps. First, we evaluate the forecast accuracy for Holt-Winters method using different input sizes. In the second step we evaluate the performance of the anomaly detector when using different methods to estimate the variance of the distribution of the residuals. The results indicate that the suggested method works well most of the time for detection of point anomalies in seasonal and trending time series data. The thesis also discusses some potential next steps which are likely to further improve the performance of this method.

The goal of this paper is to present an emotional audio-visual. Text to speech system for the Arabic Language. The system is based on two entities: un emotional audio text to speech system which generates speech depending on the input text and the desired emotion type, and un emotional Visual model which generates the talking heads, by forming the corresponding visemes. The phonemes to visemes mapping, and the emotion shaping use a 3-paramertic face model, based on the Abstract Muscle Model. We have thirteen viseme models and five emotions as parameters to the face model. The TTS produces the phonemes corresponding to the input text, the speech with the suitable prosody to include the prescribed emotion. In parallel the system generates the visemes and sends the controls to the facial model to get the animation of the talking head in real time.

GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, preparation and analysis tools. Several advanced techniques for free-energy calculations are supported. In version 5, it reaches new performance heights, through several new and enhanced parallelization algorithms. These work on every level; SIMD registers inside cores, multithreading, heterogeneous CPU-GPU acceleration, state-of-the-art 3D domain decomposition, and ensemble-level parallelization through built-in replica exchange and the separate Copernicus framework. The latest best-in-class compressed trajectory storage format is supported.

In this article the Finite Difference method is used to solve the Black Scholes equation. A second order and fourth order accurate scheme is implemented in space and evaluated. The scheme is then tried for different initial conditions. First the discontinuous pay off function of a European Call option is used. Due to the nonsmooth charac- teristics of the chosen initial conditions both schemes show an order of two. Next, the analytical solution to the Black Scholes is used when t=T/2. In this case, with a smooth initial condition, the fourth order scheme shows an order of four. Finally, the initial nonsmooth pay off function is modified by smoothing. Also in this case, the fourth order method shows an order of convergence of four.

Video compression uses encoding to reduce the number of bits that are used forrepresenting a video file in order to store and transmit it at a smaller size. Adecoder reconstructs the received data into a representation of the original video.Video coding standards determines how the video compression should beconducted and one of the latest standards is High Efficiency Video Coding (HEVC).One technique that can be used in the encoder is variance adaptive quantizationwhich improves the subjective quality in videos. The technique assigns lowerquantization parameter values to parts of the frame with low variance to increasequality, and vice versa. Another part of the encoder is the sample adaptive offsetfilter, which reduces pixel errors caused by the compression. In this project, thevariance adaptive quantization technique is implemented in the Ericsson researchHEVC encoder c65. Its functionality is verified by subjective evaluation. It isinvestigated if the sample adaptive offset can exploit the adjusted quantizationparameters values when reducing pixel errors to improve compression efficiency. Amodel for this purpose is developed and implemented in c65. Data indicates thatthe model can increase the error reduction in the sample adaptive offset. However,the difference in performance of the model compared to a reference encoder is notsignificant.

Which security holes and security methods do IEEE 802.11b and Bluetooth offer? Which standard provides best security methods for companies? These are two interesting questions that this thesis will be about. The purpose is to give companies more information of the security aspects that come with using WLANs. An introduction to the subject of WLAN is presented in order to give an overview before the description of the two WLAN standards; IEEE 802.11b and Bluetooth. The thesis will give an overview of how IEEE 802.11b and Bluetooth works, a in depth description about the security issues of the two standards will be presented, security methods available for companies, the security flaws and what can be done in order to create a secure WLAN are all important aspects to this thesis. In order to give a guidance of which WLAN standard to choose, a comparison of the two standards with the security issues in mind, from a company's point of view is described. We will present our conclusion which entails a recommendation to companies to use Bluetooth over IEEE 802.11b, since it offers better security methods.

Many embedded systems are complex, and it is often required that the firmware in these systems are updatable by the end-user. For economical and confidentiality reasons, it is important that these systems only accept firmware approved by the firmware producer.

This thesis work focuses on creating a security enhanced firmware update procedure that is suitable for use in embedded systems. The common elements of embedded systems are described and various candidate algorithms are compared as candidates for firmware verification. Patents are used as a base for the proposal of a security enhanced update procedure. We also use attack trees to perform a threat analysis on an update procedure.

The results are a threat analysis of a home office router and the proposal of an update procedure. The update procedure will only accept approved firmware and prevents reversion to old, vulnerable, firmware versions. The firmware verification is performed using the hash function SHA-224 and the digital signature algorithm RSA with a key length of 2048. The selection of algorithms and key lengths mitigates the threat of brute-force and cryptanalysis attacks on the verification algorithms and is believed to be secure through 2030.

Question Answering systems are greatly sought after in many areas of industry. Unfortunately, as most research in Natural Language Processing is conducted in English, the applicability of such systems to other languages is limited. Moreover, these systems often struggle in dealing with long text sequences.

This thesis explores the possibility of applying existing models to the Swedish language, in a domain where the syntax and semantics differ greatly from typical Swedish texts. Additionally, the text length may vary arbitrarily. To solve these problems, transfer learning techniques and state-of-the-art Question Answering models are investigated. Furthermore, a novel, divide-and-conquer based technique for processing long texts is developed.

Results show that the transfer learning is partly unsuccessful, but the system is capable of perform reasonably well in the new domain regardless. Furthermore, the system shows great performance improvement on longer text sequences with the use of the new technique.

New optical network technologies provide opportunities for fast, controllable bandwidth management. These technologies can now explicitly provide resources to data paths, creating demand driven bandwidth reservation across networks where an applications bandwidth needs can be meet almost exactly. Dynamic synchronous Transfer Mode (DTM) is a gigabit network technology that provides channels with dynamically adjustable capacity. TCP is a reliable end-to-end transport protocol that adapts its rate to the available capacity. Both TCP and the DTM bandwidth can react to changes in the network load, creating a complex system with inter-dependent feedback mechanisms. The contribution of this work is an assessment of a bandwidth allocation scheme for TCP flows on variable capacity technologies. We have created a simulation environment using ns-2 and our results indicate that the allocation of bandwidth maximises TCP throughput for most flows, thus saving valuable capacity when compared to a scheme such as link over-provisioning. We highlight one situation where the allocation scheme might have some deficiencies against the static reservation of resources, and describe its causes. This type of situation warrants further investigation to understand how the algorithm can be modified to achieve performance similar to that of the fixed bandwidth case.

The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers. This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching.

This thesis studies access patterns for TV and video and the potential for caching. The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months.

The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type.

For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands.

This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic. We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios.

The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers. This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching. This thesis studies access patterns for TV and video and the potential for caching. The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months. The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type. For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands. This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic. We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios.

Measurement and analysis of real traffic is important to gain knowledge about the characteristics of the traffic. Without measurement, it is impossible to build realistic traffic models. It is recent that data traffic was found to have self-similar properties. In this thesis work traffic captured on the network at SICS and on the Supernet, is shown to have this fractal-like behaviour. The traffic is also examined with respect to which protocols and packet sizes are present and in what proportions. In the SICS trace most packets are small, TCP is shown to be the predominant transport protocol and NNTP the most common application. In contrast to this, large UDP packets sent between not well-known ports dominates the Supernet traffic. Finally, characteristics of the client side of the WWW traffic are examined more closely. In order to extract useful information from the packet trace, web browsers use of TCP and HTTP is investigated including new features in HTTP/1.1 such as persistent connections and pipelining. Empirical probability distributions are derived describing session lengths, time between user clicks and the amount of data transferred due to a single user click. These probability distributions make up a simple model of WWW-sessions.

Several studies of Internet traffic have shown that it is a small percentage of the flows that dominate the traffic. This is often referred to as the mice and elephants phenomenon. It has been proposed that this might be one of very few invariants of Internet traffic and that this property could somehow be used for traffic engineering purposes. The idea being that one in a scalable way could control a major part of the traffic by only keeping track of a small number of flows. But for this the large flows must also be stable in the meaning that they should be among the largest flows during long periods of time. In this work we analyse packet traces of Internet traffic and study the temporal characteristics of large aggregated traffic flows defined by destination address prefixes.

Large network operators have thousands or tens of thousands of access aggregation links that they need to manage and dimension properly. Measuring and understanding the traffic characteristics on these type of links are therefore essential. What do the traffic intensity characteristics look like on different timescales from days down to milliseconds? How do the characteristics differ if we compare links with the same capacity but with different type of clients and access technologies? How do the traffic characteristics differ from that on core network links? These are the type of questions we set out to investigate in this paper. We present the results of packet level measurements on three different 1Gbit/s aggregation links in an operational IP network. We see large differences in traffic characteristics between the three links. We observe highly skewed link load probability densities on timescales relevant for buffering (i.e. 10-milliseconds). We demonstrate the existence of large traffic spikes on short timescales (10-100ms) and show their impact on link delay. We also found that these traffic bursts often are caused by only one or a few IP flows.

Today video and TV distribution dominate Internet traffic and the increasing demand for high-bandwidth multimedia services puts pressure on Internet service providers. In this paper we simulate TV distribution with time-shift and investigate the effect of introducing a local cache close to the viewers. We study what impact TV program popularity, program set size, cache replacement policy and other factors have on the caching efficiency. The simulation results show that introducing a local cache close to the viewers significantly reduces the network load from TV-on-Demand services. By caching 4% of the program volume we can decrease the peak load during prime time by almost 50%. We also show that the TV program type and how program popularity changes over time can have a big influence on cache hit ratios and the resulting link loads